Continued adherence to community-based health insurance scheme in two districts of northeast Ethiopia: application of accelerated failure time shared frailty models.
Community-based health insurance
Ethiopia
Membership adherence
Journal
International journal for equity in health
ISSN: 1475-9276
Titre abrégé: Int J Equity Health
Pays: England
ID NLM: 101147692
Informations de publication
Date de publication:
05 02 2022
05 02 2022
Historique:
received:
27
09
2021
accepted:
17
01
2022
entrez:
6
2
2022
pubmed:
7
2
2022
medline:
5
4
2022
Statut:
epublish
Résumé
The sustainability of a voluntary community-based health insurance scheme depends to a greater extent on its ability to retain members. In low- and middle-income countries, high rate of member dropout has been a great concern for such schemes. Although several studies have investigated the factors influencing dropout decisions, none of these looked into how long and why members adhere to the scheme. The purpose of this study was to determine the factors affecting time to drop out while accounting for the influence of cluster-level variables. A community-based cross-sectional study was conducted among 1232 rural households who have ever been enrolled in two community-based health insurance schemes. Data were collected using an interviewer-administered questionnaire via a mobile data collection platform. The Kaplan-Meier estimates were used to compare the time to drop out among subgroups. To identify predictors of time to drop out, a multivariable analysis was done using the accelerated failure time shared frailty models. The degree of association was assessed using the acceleration factor (δ) and statistical significance was determined at 95% confidence interval. Results of the multivariable analysis revealed that marital status of the respondents (δ = 1.610; 95% CI: 1.216, 2.130), household size (δ = 1.168; 95% CI: 1.013, 1.346), presence of chronic illness (δ = 1.424; 95% CI: 1.165, 1.740), hospitalization history (δ = 1.306; 95% CI: 1.118, 1.527), higher perceived quality of care (δ = 1.322; 95% CI: 1.100, 1.587), perceived risk protection (δ = 1.218; 95% CI: 1.027, 1.444), and higher trust in the scheme (δ = 1.731; 95% CI: 1.428, 2.098) were significant predictors of time to drop out. Contrary to the literature, wealth status did not show a significant correlation with the time to drop out. The fact that larger households and those with chronic illness remained longer in the scheme is suggestive of adverse selection. It is needed to reconsider the premium level in line with household size to attract small size households. Resolving problems related to the quality of health care can be a cross-cutting area of intervention to retain members by building trust in the scheme and enhancing the risk protection ability of the schemes.
Sections du résumé
BACKGROUND
The sustainability of a voluntary community-based health insurance scheme depends to a greater extent on its ability to retain members. In low- and middle-income countries, high rate of member dropout has been a great concern for such schemes. Although several studies have investigated the factors influencing dropout decisions, none of these looked into how long and why members adhere to the scheme. The purpose of this study was to determine the factors affecting time to drop out while accounting for the influence of cluster-level variables.
METHODS
A community-based cross-sectional study was conducted among 1232 rural households who have ever been enrolled in two community-based health insurance schemes. Data were collected using an interviewer-administered questionnaire via a mobile data collection platform. The Kaplan-Meier estimates were used to compare the time to drop out among subgroups. To identify predictors of time to drop out, a multivariable analysis was done using the accelerated failure time shared frailty models. The degree of association was assessed using the acceleration factor (δ) and statistical significance was determined at 95% confidence interval.
RESULTS
Results of the multivariable analysis revealed that marital status of the respondents (δ = 1.610; 95% CI: 1.216, 2.130), household size (δ = 1.168; 95% CI: 1.013, 1.346), presence of chronic illness (δ = 1.424; 95% CI: 1.165, 1.740), hospitalization history (δ = 1.306; 95% CI: 1.118, 1.527), higher perceived quality of care (δ = 1.322; 95% CI: 1.100, 1.587), perceived risk protection (δ = 1.218; 95% CI: 1.027, 1.444), and higher trust in the scheme (δ = 1.731; 95% CI: 1.428, 2.098) were significant predictors of time to drop out. Contrary to the literature, wealth status did not show a significant correlation with the time to drop out.
CONCLUSIONS
The fact that larger households and those with chronic illness remained longer in the scheme is suggestive of adverse selection. It is needed to reconsider the premium level in line with household size to attract small size households. Resolving problems related to the quality of health care can be a cross-cutting area of intervention to retain members by building trust in the scheme and enhancing the risk protection ability of the schemes.
Identifiants
pubmed: 35123498
doi: 10.1186/s12939-022-01620-9
pii: 10.1186/s12939-022-01620-9
pmc: PMC8817608
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16Informations de copyright
© 2022. The Author(s).
Références
Int J Equity Health. 2012 Dec 05;11:74
pubmed: 23217438
Glob Health Res Policy. 2018 Nov 13;3:32
pubmed: 30460332
Health Policy Plan. 2015 Dec;30(10):1296-306
pubmed: 25616670
Clinicoecon Outcomes Res. 2020 Dec 16;12:747-760
pubmed: 33364800
Appl Health Econ Health Policy. 2017 Apr;15(2):215-226
pubmed: 27696328
BMC Health Serv Res. 2014 Nov 20;14:538
pubmed: 25411021
Lancet. 2018 May 5;391(10132):1783-1798
pubmed: 29678341
Fam Med Community Health. 2020 Feb 16;8(1):e000262
pubmed: 32148735
Lancet Glob Health. 2018 Nov;6(11):e1196-e1252
pubmed: 30196093
Bull World Health Organ. 2017 Jun 1;95(6):465-472
pubmed: 28603313
BMC Health Serv Res. 2012 Jun 28;12:181
pubmed: 22741549
Soc Sci Med. 2014 Apr;107:78-88
pubmed: 24607669
Appl Health Econ Health Policy. 2020 Feb;18(1):81-96
pubmed: 31535352
Trop Med Int Health. 2015 Mar;20(3):312-21
pubmed: 25418283
Int J Health Policy Manag. 2019 Aug 01;8(10):593-606
pubmed: 31657186
PLoS One. 2016 Aug 31;11(8):e0160479
pubmed: 27579731
Clinicoecon Outcomes Res. 2021 May 11;13:359-375
pubmed: 34007193
PLoS One. 2021 Jul 16;16(7):e0253368
pubmed: 34270556
PLoS Med. 2009 Jul 21;6(7):e1000097
pubmed: 19621072
Health Policy. 2009 Oct;92(2-3):174-9
pubmed: 19394105
Innov Aging. 2019 Sep 04;3(4):igz036
pubmed: 31528719
Int J Equity Health. 2013 Jul 03;12:50
pubmed: 23822579
BMC Health Serv Res. 2017 Aug 7;17(1):535
pubmed: 28784123
Health Econ Rev. 2019 Jul 6;9(1):23
pubmed: 31280394
Stat Methods Med Res. 2020 Nov;29(11):3424-3454
pubmed: 32466712
Stat Med. 1997 Jan 15-Feb 15;16(1-3):215-24
pubmed: 9004393
BMC Health Serv Res. 2018 Jan 30;18(1):52
pubmed: 29378567
Int J Health Plann Manage. 2018 Oct;33(4):794-805
pubmed: 30074646
Can J Public Health. 2007 Jul-Aug;98(4):341-6
pubmed: 17896749
Health Econ. 2010 Sep;19 Suppl:5-35
pubmed: 20803629
Health Econ Rev. 2016 Dec;6(1):43
pubmed: 27624462
Bull World Health Organ. 2010 Jun;88(6):402
pubmed: 20539847
Soc Sci Med. 2019 Jan;220:112-119
pubmed: 30419495
Risk Manag Healthc Policy. 2020 Dec 04;13:2835-2844
pubmed: 33304111
Int J Equity Health. 2018 Jan 29;17(1):13
pubmed: 29378585
Glob Health Action. 2017;10(1):1287398
pubmed: 28471332
Glob Health Res Policy. 2016 Nov 22;1:18
pubmed: 29202066
Int J Health Policy Manag. 2017 Oct 17;7(5):443-454
pubmed: 29764108
Lancet. 2018 Nov 17;392(10160):2203-2212
pubmed: 30195398
Health Policy Plan. 2016 Dec;31(10):1433-1444
pubmed: 27476500
Z Gesundh Wiss. 2017;25(6):625-634
pubmed: 29177126
Health Syst Reform. 2019;5(1):32-47
pubmed: 30924747
Soc Sci Med. 2006 Sep;63(5):1236-45
pubmed: 16635541
Adv Health Econ Health Serv Res. 2009;21:107-32
pubmed: 19791701